>>Hello, everyone. Welcome to the Cube's live coverage here in Boston, Massachusetts for AWS reinforce 2022. I'm John fur, host of the cube with Dave. Valante my co-host for breaking analysis, famous podcast, Dave, great to see you. Um, Beck in Boston, 2010, we started >>The queue. It all started right here in this building. John, >>12 years ago, we started here, but here, you know, just 12 years, it just seems like a marathon with the queue. Over the years, we've seen many ways. You call yourself a historian, which you are. We are both now, historians security is doing over. And we said in 2013 is security to do where we asked pat GSK. Now the CEO of Intel prior to that, he was the CEO of VMware. This is the security show fors. It's called the reinforce. They have reinvent, which is their big show. Now they have these, what they call reshow, re Mars, machine learning, automation, um, robotics and space. And then they got reinforced, which is security. It's all about security in the cloud. So great show. Lot of talk about the keynotes were, um, pretty, I wouldn't say generic on one hand, but specific in the other clear AWS posture, we were both watching. What's your take? >>Well, John, actually looking back to may of 2010, when we started the cube at EMC world, and that was the beginning of this massive boom run, uh, which, you know, finally, we're starting to see some, some cracks of the armor. Of course, we're threats of recession. We're in a recession, most likely, uh, in inflationary pressures, interest rate hikes. And so, you know, finally the tech market has chilled out a little bit and you have this case before we get into the security piece of is the glass half full or half empty. So budgets coming into this year, it was expected. They would grow at a very robust eight point half percent CIOs have tuned that down, but it's still pretty strong at around 6%. And one of the areas that they really have no choice, but to focus on is security. They moved everything into the cloud or a lot of stuff into the cloud. >>They had to deal with remote work and that created a lot of security vulnerabilities. And they're still trying to figure that out and plug the holes with the lack of talent that they have. So it's interesting re the first reinforc that we did, which was also here in 2019, Steven Schmidt, who at the time was chief information security officer at Amazon web services said the state of cloud security is really strong. All this narrative, like the pat Gelsinger narrative securities, a do over, which you just mentioned, security is broken. It doesn't help the industry. The state of cloud security is very strong. If you follow the prescription. Well, see, now Steven Schmidt, as you know, is now chief security officer at Amazon. So we followed >>Jesse all Amazon, not just AWS. So >>He followed Jesse over and I asked him, well, why no, I, and they said, well, he's responsible now for physical security. Presumably the warehouses I'm like, well, wait a minute. What about the data centers? Who's responsible for that? So it's kind of funny, CJ. Moses is now the CSO at AWS and you know, these events are, are good. They're growing. And it's all about best practices, how to apply the practices. A lot of recommendations from, from AWS, a lot of tooling and really an ecosystem because let's face it. Amazon doesn't have the breadth and depth of tools to do it alone. >>And also the attendance is interesting, cuz we are just in New York city for the, uh, ado summit, 19,000 people, massive numbers, certainly in the pandemic. That's probably one of the top end shows and it was a summit. This is a different audience. It's security. It's really nerdy. You got OT, you got cloud. You've got on-prem. So now you have cloud operations. We're calling super cloud. Of course we're having our inaugural pilot event on August 9th, check it out. We're called super cloud, go to the cube.net to check it out. But this is the super cloud model evolving with security. And what you're hearing today, Dave, I wanna get your reaction to this is things like we've got billions of observational points. We're certainly there's no perimeter, right? So the perimeter's dead. The new perimeter, if you will, is every transaction at scale. So you have to have a new model. So security posture needs to be rethought. They actually said that directly on the keynote. So security, although numbers aren't as big as last week or two weeks ago in New York still relevant. So alright. There's sessions here. There's networking. Very interesting demographic, long hair. Lot of >>T-shirts >>No lot of, not a lot of nerds doing to build out things over there. So, so I gotta ask you, what's your reaction to this scale as the new advantage? Is that a tailwind or a headwind? What's your read? >>Well, it is amazing. I mean he actually, Steven Schmidt talked about quadrillions of events every month, quadrillions 15 zeros. What surprised me, John. So they, they, Amazon talks about five areas, but by the, by the way, at the event, they got five tracks in 125 sessions, data protection and privacy, GRC governance, risk and compliance, identity network security and threat detection. I was really surprised given the focus on developers, they didn't call out container security. I would've thought that would be sort of a separate area of focus, but to your point about scale, it's true. Amazon has a scale where they'll see events every day or every month that you might not see in a generation if you just kind of running your own data center. So I do think that's, that's, that's, that's a, a, a, a valid statement having said that Amazon's got a limited capability in terms of security. That's why they have to rely on the ecosystem. Now it's all about APIs connecting in and APIs are one of the biggest security vulnerability. So that's kind of, I, I I'm having trouble squaring that circle. >>Well, they did just to come up, bring back to the whole open source and software. They did say they did make a measurement was store, but at the beginning, Schmidt did say that, you know, besides scale being an advantage for Amazon with a quadri in 15 zeros, don't bolt on security. So that's a classic old school. We've heard that before, right. But he said specifically, weave in security in the dev cycles. And the C I C D pipeline that is, that basically means shift left. So sneak is here, uh, company we've covered. Um, and they, their whole thing is shift left. That implies Docker containers that implies Kubernetes. Um, but this is not a cloud native show per se. It's much more crypto crypto. You heard about, you know, the, uh, encrypt everything message on the keynote. You heard, um, about reasoning, quantum, quantum >>Skating to the puck. >>Yeah. So yeah, so, you know, although the middleman is logged for J heard that little little mention, I love the quote from Lewis Hamilton that they put up on stage CJ, Moses said, team behind the scenes make it happen. So a big emphasis on teamwork, big emphasis on don't bolt on security, have it in the beginning. We've heard that before a lot of threat modeling discussions, uh, and then really this, you know, the news around the cloud audit academy. So clearly skills gap, more threats, more use cases happening than ever before. >>Yeah. And you know, to your point about, you know, the teamwork, I think the problem that CISOs have is they just don't have the talent to that. AWS has. So they have a real difficulty applying that talent. And so but's saying, well, join us at these shows. We'll kind of show you how to do it, how we do it internally. And again, I think when you look out on this ecosystem, there's still like thousands and thousands of tools that practitioners have to apply every time. There's a tool, there's a separate set of skills to really understand that tool, even within AWS's portfolio. So this notion of a shared responsibility model, Amazon takes care of, you know, securing for instance, the physical nature of S3 you're responsible for secure, make sure you're the, the S3 bucket doesn't have public access. So that shared responsibility model is still very important. And I think practitioners still struggling with all this complexity in this matrix of tools. >>So they had the layered defense. So, so just a review opening keynote with Steve Schmidt, the new CSO, he talked about weaving insecurity in the dev cycles shift left, which is the, I don't bolt it on keep in the beginning. Uh, the lessons learned, he talked a lot about over permissive creates chaos, um, and that you gotta really look at who has access to what and why big learnings there. And he brought up the use cases. The more use cases are coming on than ever before. Um, layered defense strategy was his core theme, Dave. And that was interesting. And he also said specifically, no, don't rely on single security control, use multiple layers, stronger together. Be it it from the beginning, basically that was the whole ethos, the posture, he laid that down >>And he had a great quote on that. He said, I'm sorry to interrupt single controls. And binary states will fail guaranteed. >>Yeah, that's a guarantee that was basically like, that's his, that's not a best practice. That's a mandate. <laugh> um, and then CJ, Moses, who was his deputy in the past now takes over a CSO, um, ownership across teams, ransomware mitigation, air gaping, all that kind of in the weeds kind of security stuff. You want to check the boxes on. And I thought he did a good job. Right. And he did the news. He's the new CISO. Okay. Then you had lean is smart from Mongo DB. Come on. Yeah. Um, she was interesting. I liked her talk, obviously. Mongo is one of the ecosystem partners headlining game. How do you read into that? >>Well, I, I I'm, its really interesting. Right? You didn't see snowflake up there. Right? You see data breaks up there. You had Mongo up there and I'm curious is her and she's coming on the cube tomorrow is her primary role sort of securing Mongo internally? Is it, is it securing the Mongo that's running across clouds. She's obviously here talking about AWS. So what I make of it is, you know, that's, it's a really critical partner. That's driving a lot of business for AWS, but at the same time it's data, they talked about data security being one of the key areas that you have to worry about and that's, you know what Mongo does. So I'm really excited. I talked to her >>Tomorrow. I, I did like her mention a big idea, a cube alumni, yeah. Company. They were part of our, um, season one of our eight of us startup showcase, check out AWS startups.com. If you're watching this, we've been doing now, we're in season two, we're featuring the fastest growing hottest startups in the ecosystem. Not the big players, that's ISVs more of the startups. They were mentioned. They have a great product. So I like to mention a big ID. Um, security hub mentioned a config. They're clearly a big customer and they have user base, a lot of E C, two and storage going on. People are building on Mongo so I can see why they're in there. The question I want to ask you is, is Mongo's new stuff in line with all the upgrades in the Silicon. So you got graviton, which has got great stuff. Um, great performance. Do you see that, that being a key part of things >>Well, specifically graviton. So I I'll tell you this. I'll tell you what I know when you look at like snowflake, for instance, is optimizing for graviton. For certain workloads, they actually talked about it on their earnings call, how it's lowered the cost for customers and actually hurt their revenue. You know, they still had great revenue, but it hurt their revenue. My sources indicate to me that that, that Mongo is not getting as much outta graviton two, but they're waiting for graviton three. Now they don't want to make that widely known because they don't wanna dis AWS. But it's, it's probably because Mongo's more focused on analytics. But so to me, graviton is the future. It's lower cost. >>Yeah. Nobody turns off the database. >>Nobody turns off the database. >><laugh>, it's always cranking C two cycles. You >>Know the other thing I wanted to bring, bring up, I thought we'd hear, hear more about ransomware. We heard a little bit of from Kirk Coel and he, and he talked about all these things you could do to mitigate ransomware. He didn't talk about air gaps and that's all you hear is how air gap. David Flo talks about this all the time. You must have air gaps. If you wanna, you know, cover yourself against ransomware. And they didn't even mention that. Now, maybe we'll hear that from the ecosystem. That was kind of surprising. Then I, I saw you made a note in our shared doc about encryption, cuz I think all the talk here is encryption at rest. What about data in motion? >>Well, this, this is the last guy that came on the keynote. He brought up encryption, Kurt, uh, Goel, which I love by the way he's VP of platform. I like his mojo. He's got the long hair >>And he's >>Geeking out swagger, but I, he hit on some really cool stuff. This idea of the reasoning, right? He automated reasoning is little pet project that is like killer AI. That's next generation. Next level >>Stuff. Explain that. >>So machine learning does all kinds of things, you know, goes to sit pattern, supervise, unsupervised automate stuff, but true reasoning. Like no one connecting the dots with software. That's like true AI, right? That's really hard. Like in word association, knowing how things are connected, looking at pattern and deducing things. So you predictive analytics, we all know comes from great machine learning. But when you start getting into deduction, when you say, Hey, that EC two cluster never should be on the same VPC, is this, this one? Why is this packet trying to go there? You can see patterns beyond normal observation space. So if you have a large observation space like AWS, you can really put some killer computer science technology on this. And that's where this reasoning is. It's next level stuff you don't hear about it because nobody does it. Yes. I mean, Google does it with metadata. There's meta meta reasoning. Um, we've been, I've been watching this for over two decades now. It's it's a part of AI that no one's tapped and if they get it right, this is gonna be a killer part of the automation. So >>He talked about this, basically it being advanced math that gets you to provable security, like you gave an example. Another example I gave is, is this S3 bucket open to the public is a, at that access UN restricted or unrestricted, can anyone access my KMS keys? So, and you can prove, yeah. The answer to that question using advanced math and automated reasoning. Yeah, exactly. That's a huge leap because you used to be use math, but you didn't have the data, the observation space and the compute power to be able to do it in near real time or real time. >>It's like, it's like when someone, if in the physical world real life in real life, you say, Hey, that person doesn't belong here. Or you, you can look at something saying that doesn't fit <laugh> >>Yeah. Yeah. >>So you go, okay, you observe it and you, you take measures on it or you query that person and say, why you here? Oh, okay. You're here. It doesn't fit. Right. Think about the way on the right clothes, the right look, whatever you kind of have that data. That's deducing that and getting that information. That's what reasoning is. It's it's really a killer level. And you know, there's encrypt, everything has to be data. Lin has to be data in at movement at rest is one thing, but you gotta get data in flight. Dave, this is a huge problem. And making that work is a key >>Issue. The other thing that Kirk Coel talked about was, was quantum, uh, quantum proof algorithms, because basically he put up a quote, you're a hockey guy, Wayne Greski. He said the greatest hockey player ever. Do you agree? I do agree. Okay, great. >>Bobby or, and Wayne Greski. >>Yeah, but okay, so we'll give the nada Greski, but I always skate to the where the puck is gonna be not to where it's been. And basically his point was where skating to where quantum is going, because quantum, it brings risks to basically blow away all the existing crypto cryptographic algorithms. I, I, my understanding is N just came up with new algorithms. I wasn't clear if those were supposed to be quantum proof, but I think they are, and AWS is testing them. And AWS is coming out with, you know, some test to see if quantum can break these new algos. So that's huge. The question is interoperability. Yeah. How is it gonna interact with all the existing algorithms and all the tools that are out there today? So I think we're a long way off from solving that problem. >>Well, that was one of Kurt's big point. You talking about quantum resistant cryptography and they introduce hybrid post quantum key agreements. That means KMS cert certification, cert manager and manager all can manage the keys. This was something that's gives more flexibility on, on, on that quantum resistance argument. I gotta dig into it. I really don't know how it works, what he meant by that in terms of what does that hybrid actually mean? I think what it means is multi mode and uh, key management, but we'll see. >>So I come back to the ho the macro for a second. We've got consumer spending under pressure. Walmart just announced, not great earning. Shouldn't be a surprise to anybody. We have Amazon meta and alphabet announcing this weekend. I think Microsoft. Yep. So everybody's on edge, you know, is this gonna ripple through now? The flip side of that is BEC because the economy yeah. Is, is maybe not in, not such great shape. People are saying maybe the fed is not gonna raise after September. Yeah. So that's, so that's why we come back to this half full half empty. How does that relate to cyber security? Well, people are prioritizing cybersecurity, but it's not an unlimited budget. So they may have to steal from other places. >>It's a double whammy. Dave, it's a double whammy on the spend side and also the macroeconomic. So, okay. We're gonna have a, a recession that's predicted the issue >>On, so that's bad on the one hand, but it's good from a standpoint of not raising interest rates, >>It's one of the double whammy. It was one, it's one of the double whammy and we're talking about here, but as we sit on the cube two weeks ago at <inaudible> summit in New York, and we did at re Mars, this is the first recession where the cloud computing hyperscale is, are pumping full cylinder, all cylinders. So there's a new economic engine called cloud computing that's in place. So unlike data center purchase in the past, that was CapEx. When, when spending was hit, they pause was a complete shutdown. Then a reboot cloud computer. You can pause spending for a little bit, make, might make the cycle longer in sales, but it's gonna be quickly fast turned on. So, so turning off spending with cloud is not that hard to do. You can hit pause and like check things out and then turn it back on again. So that's just general cloud economics with security though. I don't see the spending slowing down. Maybe the sales cycles might go longer, but there's no spending slow down in my mind that I see. And if there's any pause, it's more of refactoring, whether it's the crypto stuff or new things that Amazon has. >>So, so that's interesting. So a couple things there. I do think you're seeing a slight slow down in the, the, the ex the velocity of the spend. When you look at the leaders in spending velocity in ETR data, CrowdStrike, Okta, Zscaler, Palo Alto networks, they're all showing a slight deceleration in spending momentum, but still highly elevated. Yeah. Okay. So, so that's a, I think now to your other point, really interesting. What you're saying is cloud spending is discretionary. That's one of the advantages. I can dial it down, but track me if I'm wrong. But most of the cloud spending is with reserved instances. So ultimately you're buying those reserved instances and you have to spend over a period of time. So they're ultimately AWS is gonna see that revenue. They just might not see it for this one quarter. As people pull back a little bit, right. >>It might lag a little bit. So it might, you might not see it for a quarter or two, so it's impact, but it's not as severe. So the dialing up, that's a key indicator get, I think I'm gonna watch that because that's gonna be something that we've never seen before. So what's that reserve now the wild card and all this and the dark horse new services. So there's other services besides the classic AC two, but security and others. There's new things coming out. So to me, this is absolutely why we've been saying super cloud is a thing because what's going on right now in security and cloud native is there's net new functionality that needs to be in place to handle multiple clouds, multiple abstraction layers, and to do all these super cloudlike capabilities like Mike MongoDB, like these vendors, they need to up their gain. And that we're gonna see new cloud native services that haven't exist. Yeah. I'll use some hatchy Corp here. I'll use something over here. I got some VMware, I got this, but there's gaps. Dave, there'll be gaps that are gonna emerge. And I think that's gonna be a huge wild >>Cup. And now I wanna bring something up on the super cloud event. So you think about the layers I, as, uh, PAs and, and SAS, and we see super cloud permeating, all those somebody ask you, well, because we have Intuit coming on. Yep. If somebody asks, why Intuit in super cloud, here's why. So we talked about cloud being discretionary. You can dial it down. We saw that with snowflake sort of Mongo, you know, similarly you can, if you want dial it down, although transaction databases are to do, but SAS, the SAS model is you pay for it every month. Okay? So I've, I've contended that the SAS model is not customer friendly. It's not cloudlike and it's broken for customers. And I think it's in this decade, it's gonna get fixed. And people are gonna say, look, we're gonna move SAS into a consumption model. That's more customer friendly. And that's something that we're >>Gonna explore in the super cloud event. Yeah. And one more thing too, on the spend, the other wild card is okay. If we believe super cloud, which we just explained, um, if you don't come to the August 9th event, watch the debate happen. But as the spending gets paused, the only reason why spending will be paused in security is the replatforming of moving from tools to platforms. So one of the indicators that we're seeing with super cloud is a flight to best of breeds on platforms, meaning hyperscale. So on Amazon web services, there's a best of breed set of services from AWS and the ecosystem on Azure. They have a few goodies there and customers are making a choice to use Azure for certain things. If they, if they have teams or whatever or office, and they run all their dev on AWS. So that's kind of what's happened. So that's, multi-cloud by our definition is customers two clouds. That's not multi-cloud, as in things are moving around. Now, if you start getting data planes in there, these customers want platforms. If I'm a cybersecurity CSO, I'm moving to platforms, not just tools. So, so maybe CrowdStrike might have it dial down, but a little bit, but they're turning into a platform. Splunk trying to be a platform. Okta is platform. Everybody's scale is a platform. It's a platform war right now, Dave cyber, >>A right paying identity. They're all plat platform, beach products. We've talked about that a lot in the queue. >>Yeah. Well, great stuff, Dave, let's get going. We've got two days alive coverage. Here is a cubes at, in Boston for reinforc 22. I'm Shante. We're back with our guests coming on the queue at the short break.

>>Okay. Okay. We're back at Caesar's Forum. The Snowflake summit 2022. The cubes. Continuous coverage this day to wall to wall coverage. We're so excited to have the analyst panel here, some of my colleagues that we've done a number. You've probably seen some power panels that we've done. David McGregor is here. He's the senior vice president and research director at Ventana Research. To his left is Tony Blair, principal at DB Inside and my in the co host seat. Sanjeev Mohan Sanremo. Guys, thanks so much for coming on. I'm glad we can. Thank you. You're very welcome. I wasn't able to attend the analyst action because I've been doing this all all day, every day. But let me start with you, Dave. What have you seen? That's kind of interested you. Pluses, minuses. Concerns. >>Well, how about if I focus on what I think valuable to the customers of snowflakes and our research shows that the majority of organisations, the majority of people, do not have access to analytics. And so a couple of things they've announced I think address those are helped to address those issues very directly. So Snow Park and support for Python and other languages is a way for organisations to embed analytics into different business processes. And so I think that will be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most most people in the organisation or not, analysts, they're doing some line of business function. Their HR managers, their marketing people, their salespeople, their finance people right there, not sitting there mucking around in the data. They're doing a job and they need analytics in that job. So, >>Tony, I thank you. I've heard a lot of data mesh talk this week. It's kind of funny. Can't >>seem to get away from it. You >>can't see. It seems to be gathering momentum, but But what have you seen? That's been interesting. >>What I have noticed. Unfortunately, you know, because the rooms are too small, you just can't get into the data mesh sessions, so there's a lot of interest in it. Um, it's still very I don't think there's very much understanding of it, but I think the idea that you can put all the data in one place which, you know, to me, stuff like it seems to be kind of sort of in a way, it sounds like almost like the Enterprise Data warehouse, you know, Clouded Cloud Native Edition, you know, bring it all in one place again. Um, I think it's providing, sort of, You know, it's I think, for these folks that think this might be kind of like a a linchpin for that. I think there are several other things that actually that really have made a bigger impression on me. Actually, at this event, one is is basically is, um we watch their move with Eunice store. Um, and it's kind of interesting coming, you know, coming from mongo db last week. And I see it's like these two companies seem to be going converging towards the same place at different speeds. I think it's not like it's going to get there faster than Mongo for a number of different reasons, but I see like a number of common threads here. I mean, one is that Mongo was was was a company. It's always been towards developers. They need you know, start cultivating data, people, >>these guys going the other way. >>Exactly. Bingo. And the thing is that but they I think where they're converging is the idea of operational analytics and trying to serve all constituencies. The other thing, which which also in terms of serving, you know, multiple constituencies is how snowflake is laid out Snow Park and what I'm finding like. There's an interesting I economy. On one hand, you have this very ingrained integration of Anaconda, which I think is pretty ingenious. On the other hand, you speak, let's say, like, let's say the data robot folks and say, You know something our folks wanna work data signs us. We want to work in our environment and use snowflake in the background. So I see those kind of some interesting sort of cross cutting trends. >>So, Sandy, I mean, Frank Sullivan, we'll talk about there's definitely benefits into going into the walled garden. Yeah, I don't think we dispute that, but we see them making moves and adding more and more open source capabilities like Apache iceberg. Is that a Is that a move to sort of counteract the narrative that the data breaks is put out there. Is that customer driven? What's your take on that? >>Uh, primarily I think it is to contract this whole notion that once you move data into snowflake, it's a proprietary format. So I think that's how it started. But it's hugely beneficial to the customers to the users, because now, if you have large amounts of data in parquet files, you can leave it on s three. But then you using the the Apache iceberg table format. In a snowflake, you get all the benefits of snowflakes. Optimizer. So, for example, you get the, you know, the micro partitioning. You get the meta data. So, uh, in a single query, you can join. You can do select from a snowflake table union and select from iceberg table, and you can do store procedures, user defined functions. So I think they what they've done is extremely interesting. Uh, iceberg by itself still does not have multi table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache iceberg in a raw format, they don't have it. But snowflake does, >>right? There's hence the delta. And maybe that maybe that closes over time. I want to ask you as you look around this I mean the ecosystems pretty vibrant. I mean, it reminds me of, like reinvent in 2013, you know? But then I'm struck by the complexity of the last big data era and a dupe and all the different tools. And is this different, or is it the sort of same wine new new bottle? You guys have any thoughts on that? >>I think it's different and I'll tell you why. I think it's different because it's based around sequel. So if back to Tony's point, these vendors are coming at this from different angles, right? You've got data warehouse vendors and you've got data lake vendors and they're all going to meet in the middle. So in your case, you're taught operational analytical. But the same thing is true with Data Lake and Data Warehouse and Snowflake no longer wants to be known as the Data Warehouse. There a data cloud and our research again. I like to base everything off of that. >>I love what our >>research shows that organisation Two thirds of organisations have sequel skills and one third have big data skills, so >>you >>know they're going to meet in the middle. But it sure is a lot easier to bring along those people who know sequel already to that midpoint than it is to bring big data people to remember. >>Mrr Odula, one of the founders of Cloudera, said to me one time, John Kerry and the Cube, that, uh, sequel is the killer app for a Yeah, >>the difference at this, you know, with with snowflake, is that you don't have to worry about taming the zoo. Animals really have thought out the ease of use, you know? I mean, they thought about I mean, from the get go, they thought of too thin to polls. One is ease of use, and the other is scale. And they've had. And that's basically, you know, I think very much differentiates it. I mean, who do have the scale, but it didn't have the ease of use. But don't I >>still need? Like, if I have, you know, governance from this vendor or, you know, data prep from, you know, don't I still have to have expertise? That's sort of distributed in those those worlds, right? I mean, go ahead. Yeah. >>So the way I see it is snowflake is adding more and more capabilities right into the database. So, for example, they've they've gone ahead and added security and privacy so you can now create policies and do even set level masking, dynamic masking. But most organisations have more than snowflake. So what we are starting to see all around here is that there's a whole series of data catalogue companies, a bunch of companies that are doing dynamic data masking security and governance data observe ability, which is not a space snowflake has gone into. So there's a whole ecosystem of companies that that is mushrooming, although, you know so they're using the native capabilities of snowflake, but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other, like relational databases, you can run these cross platform capabilities in that layer. So so that way, you know, snowflakes done a great job of enabling that ecosystem about >>the stream lit acquisition. Did you see anything here that indicated there making strong progress there? Are you excited about that? You're sceptical. Go ahead. >>And I think it's like the last mile. Essentially. In other words, it's like, Okay, you have folks that are basically that are very, very comfortable with tableau. But you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency, um, to San James Point. I think part of it, this kind of plays into it is what makes this different from the ado Pere is the fact that this all these capabilities, you know, a lot of vendors are taking it very seriously to make put this native obviously snowflake acquired stream. Let's so we can expect that's extremely capabilities are going to be native. >>And the other thing, too, about the Hadoop ecosystem is Claudia had to help fund all those different projects and got really, really spread thin. I want to ask you guys about this super cloud we use. Super Cloud is this sort of metaphor for the next wave of cloud. You've got infrastructure aws, azure, Google. It's not multi cloud, but you've got that infrastructure you're building a layer on top of it that hides the underlying complexities of the primitives and the a p I s. And you're adding new value in this case, the data cloud or super data cloud. And now we're seeing now is that snowflake putting forth the notion that they're adding a super path layer. You can now build applications that you can monetise, which to me is kind of exciting. It makes makes this platform even less discretionary. We had a lot of talk on Wall Street about discretionary spending, and that's not discretionary. If you're monetising it, um, what do you guys think about that? Is this something that's that's real? Is it just a figment of my imagination, or do you see a different way of coming any thoughts on that? >>So, in effect, they're trying to become a data operating system, right? And I think that's wonderful. It's ambitious. I think they'll experience some success with that. As I said, applications are important. That's a great way to deliver information. You can monetise them, so you know there's there's a good economic model around it. I think they will still struggle, however, with bringing everything together onto one platform. That's always the challenge. Can you become the platform that's hard, hard to predict? You know, I think this is This is pretty exciting, right? A lot of energy, a lot of large ecosystem. There is a network effect already. Can they succeed in being the only place where data exists? You know, I think that's going to be a challenge. >>I mean, the fact is, I mean, this is a classic best of breed versus the umbrella play. The thing is, this is nothing new. I mean, this is like the you know, the old days with enterprise applications were basically oracle and ASAP vacuumed up all these. You know, all these applications in their in their ecosystem, whereas with snowflake is. And if you look at the cloud, folks, the hyper scale is still building out their own portfolios as well. Some are, You know, some hyper skills are more partner friendly than others. What? What Snowflake is saying is that we're going to give all of you folks who basically are competing against the hyper skills in various areas like data catalogue and pipelines and all that sort of wonderful stuff will make you basically, you know, all equal citizens. You know the burden is on you to basically we will leave. We will lay out the A P. I s Well, we'll allow you to basically, you know, integrate natively to us so you can provide as good experience. But the but the onus is on your back. >>Should the ecosystem be concerned, as they were back to reinvent 2014 that Amazon was going to nibble away at them or or is it different? >>I find what they're doing is different. Uh, for example, data sharing. They were the first ones out the door were data sharing at a large scale. And then everybody has jumped in and said, Oh, we also do data sharing. All the hyper scholars came in. But now what snowflake has done is they've taken it to the next level. Now they're saying it's not just data sharing. It's up sharing and not only up sharing. You can stream the thing you can build, test deploy, and then monetise it. Make it discoverable through, you know, through your marketplace >>you can monetise it. >>Yes. Yeah, so So I I think what they're doing is they are taking it a step further than what hyper scale as they are doing. And because it's like what they said is becoming like the data operating system You log in and you have all of these different functionalities you can do in machine learning. Now you can do data quality. You can do data preparation and you can do Monetisation. Who do you >>think is snowflakes? Biggest competitor? What do you guys think? It's a hard question, isn't it? Because you're like because we all get the we separate computer from storage. We have a cloud data and you go, Okay, that's nice, >>but there's, like, a crack. I think >>there's uniqueness. I >>mean, put it this way. In the old days, it would have been you know, how you know the prime household names. I think today is the hyper scholars and the idea what I mean again, this comes down to the best of breed versus by, you know, get it all from one source. So where is your comfort level? Um, so I think they're kind. They're their co op a Titian the hyper scale. >>Okay, so it's not data bricks, because why they're smaller. >>Well, there is some okay now within the best of breed area. Yes, there is competition. The obvious is data bricks coming in from the data engineering angle. You know, basically the snowflake coming from, you know, from the from the data analyst angle. I think what? Another potential competitor. And I think Snowflake, basically, you know, admitted as such potentially is mongo >>DB. Yeah, >>Exactly. So I mean, yes, there are two different levels of sort >>of a on a longer term collision course. >>Exactly. Exactly. >>Sort of service now and in salesforce >>thing that was that we actually get when I say that a lot of people just laughed. I was like, No, you're kidding. There's no way. I said Excuse me, >>But then you see Mongo last week. We're adding some analytics capabilities and always been developers, as you say, and >>they trashed sequel. But yet they finally have started to write their first real sequel. >>We have M c M Q. Well, now we have a sequel. So what >>were those numbers, >>Dave? Two thirds. One third. >>So the hyper scale is but the hyper scale urz are you going to trust your hyper scale is to do your cross cloud. I mean, maybe Google may be I mean, Microsoft, perhaps aws not there yet. Right? I mean, how important is cross cloud, multi cloud Super cloud Whatever you want to call it What is your data? >>Shows? Cloud is important if I remember correctly. Our research shows that three quarters of organisations are operating in the cloud and 52% are operating across more than one cloud. So, uh, two thirds of the organisations are in the cloud are doing multi cloud, so that's pretty significant. And now they may be operating across clouds for different reasons. Maybe one application runs in one cloud provider. Another application runs another cloud provider. But I do think organisations want that leverage over the hyper scholars right they want they want to be able to tell the hyper scale. I'm gonna move my workloads over here if you don't give us a better rate. Uh, >>I mean, I I think you know, from a database standpoint, I think you're right. I mean, they are competing against some really well funded and you look at big Query barely, you know, solid platform Red shift, for all its faults, has really done an amazing job of moving forward. But to David's point, you know those to me in any way. Those hyper skills aren't going to solve that cross cloud cloud problem, right? >>Right. No, I'm certainly >>not as quickly. No. >>Or with as much zeal, >>right? Yeah, right across cloud. But we're gonna operate better on our >>Exactly. Yes. >>Yes. Even when we talk about multi cloud, the many, many definitions, like, you know, you can mean anything. So the way snowflake does multi cloud and the way mongo db two are very different. So a snowflake says we run on all the hyper scalar, but you have to replicate your data. What Mongo DB is claiming is that one cluster can have notes in multiple different clouds. That is right, you know, quite something. >>Yeah, right. I mean, again, you hit that. We got to go. But, uh, last question, um, snowflake undervalued, overvalued or just about right >>in the stock market or in customers. Yeah. Yeah, well, but, you know, I'm not sure that's the right question. >>That's the question I'm asking. You know, >>I'll say the question is undervalued or overvalued for customers, right? That's really what matters. Um, there's a different audience. Who cares about the investor side? Some of those are watching, but But I believe I believe that the from the customer's perspective, it's probably valued about right, because >>the reason I I ask it, is because it has so hyped. You had $100 billion value. It's the past service now is value, which is crazy for this student Now. It's obviously come back quite a bit below its IPO price. So But you guys are at the financial analyst meeting. Scarpelli laid out 2029 projections signed up for $10 billion.25 percent free time for 20% operating profit. I mean, they better be worth more than they are today. If they do >>that. If I If I see the momentum here this week, I think they are undervalued. But before this week, I probably would have thought there at the right evaluation, >>I would say they're probably more at the right valuation employed because the IPO valuation is just such a false valuation. So hyped >>guys, I could go on for another 45 minutes. Thanks so much. David. Tony Sanjeev. Always great to have you on. We'll have you back for sure. Having us. All right. Thank you. Keep it right there. Were wrapping up Day two and the Cube. Snowflake. Summit 2022. Right back. Mm. Mhm.

>>From around the globe, it's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Oh, great. To have you with us here on the cube, as we continue our coverage of AWS reinvent 2020, doing it virtually of course, uh, out of a necessity as I'm sure all of you can appreciate we're joined now by Alex Sanchez, who is the head of cross GDC networks and Fujitsu and Fujitsu provider of global it services and solutions. And so their footprint, um, again, is, is around the world. Uh, Alex, thanks for joining us here on the cube. We appreciate your time. And, uh, I'd like to hear a little bit more about your role first off before we jump in and tell us a little bit about Fujitsu for those who might not be familiar with it. >>Thank you very much, Sean. I really appreciate it. Uh, well, uh, first, uh, let me start by providing some background on Fujitsu. We're a global it digital transformation company offering a full range of technology products, solutions, and services. Uh, we exist to keep our customer's business running and we strive to give the best possible experience across every customer touch point. My role as head of cross CDC networks, uh, makes me in charge of standardizing technology networks across our global delivery centers. And for the past couple of years, I have been working on the standardization of our contact center platform across all of our global delivery centers. >>Yeah, yeah. I mean, you mentioned global delivery centers, so let's, let's jump into that. Uh, first off, what are they, um, you know, how have you structured your business in that respect and, um, ultimately what kind of service or a solution are they providing to your customers? >>Absolutely. So our global delivery centers are interconnected, integrated global teams. Uh, we deliver a broad portfolio of standardized services, which includes cybersecurity workplace and much more. We're based out of, uh, eight different key countries. We serve customers in over 100 and uh, different countries and we provide support in over 40 different languages. Uh, we enabled, uh, those CDCs enabled us to consistently and resilient provide services to our customers, uh, 24 seven 365 days of the year. Uh, the service, uh, that we offer, uh, as, uh, for you to global delivery teams are constructed from fully standardized components. Uh, it allows us to, uh, be configured to meet our customer needs and deliver a flawless global consistency services. >>You just, you were just talking about multiple languages, right? You've got to deal with countries, uh, environments, uh, continents, uh, businesses with different needs of, of all, you know, all over the, over the map. If you might say that, um, how do you balance that? Or how do you approach that when you do have so many customers in a wide variety of venues with a wide variety of needs and yet, you know, you want to provide for them that exemplary service that they expect when they come to Fujitsu? >>Uh, well, yes, as I mentioned, uh, we strive to evolve our contact centers so that it meets that global need that global expansion. And we adapt to our customers' needs. Uh, we have our GDCs with teams that are engaged and enabled so that we can provide customers with, uh, the best customer experience we like to help our customers reimagine their employee experience. >>Yeah. You mentioned, uh, you're talking about the contact centers and I know that you're going through this major transformation right now, in terms of, of, uh, how they're operating, um, before we get into that and, and, and jump a little bit deeper into what you've already touched on, what was the problem before, or, you know, there's always a problem, right? We're always trying to solve something, make something better, put a little finer point on that in terms of, of what you were doing before, you know, where were we? >>Well, uh, if we get to this global delivery organization, uh, tries to build trust at every opportunity we aim to deepen our customer relationships by adding a value of mix, uh, of rock, solid delivery, innovation and collaboration. However, some of our previous systems, the net always offer us the functionality and flexibility that we needed to provide a diverse range of, uh, services to our customers and what they required. So that is the basis of our, uh, challenges and, uh, what we were striving to overcome. >>So you've, you've turned AWS, um, uh, again, Amazon connect, I know that, uh, that you've got widely deployed. What was it that, that attracted you to that in terms of finding the value in it, and then what kind of efficiencies and what kinds of improvement in your operations is, is connect providing you >>Well, uh, being able to, uh, think about the art of the possible adding value to our customers. Introducing next generation features, uh, our road with AWS connected started as a two month proof of concept, uh, with over 150 different agents initially supported out of one of those global delivery centers, providing support and services to, uh, one of the regions. So, uh, we started as a way to innovate and provide next generation functionality. >>Yeah. Proof of concept periods are always interesting, aren't they? Because you, you think you're going to find out some thing and, and you might, but then you sometimes find out something else, right. That, that you're like, okay, well, the, uh, there's another application here. There's another service here. There's another layer here. Um, what was it in that period of time for you then, as far as your takeaways that convinced you that, you know, this is right, this is good. We need this. And, and so we're going to jump in. Absolutely. So, >>Uh, I would say that one of those things is that we made marked improvements in our customer experience. We were able to rapidly onboard new agents and provide automated features, such as call recording sentiment analysis, integrated callback features. We were able to help our customers faster while simultaneously improving the service quality. >>Yeah. COVID, uh, has been, um, certainly wreaking havoc in, in every facet of life. Right. Um, no question personally, professionally unit, multiple industries. So how about the impact on your, in your world first off, just from, from COVID-19, uh, how you've had to assess what your client's needs are, how you, what your needs are and, and first off, how you've, how have you balanced that >>In the past year? Yes, well, uh, Fujitsu was able to move, uh, 95% of our contact survey agents to remote work environment, equipped with the tools that they needed to provide, uh, services while remaining safe and productive. Our contact center agents and operations was not able to persist, but actually thrive during the COVID 19 pandemic and provide the much needed support that our customers were expecting and, uh, provided from, from us. How fast >>Was it, you know, I guess it required, what, how quickly did you have to respond? Cause, uh, you know, I mean, this certainly has caught a lot of, or caught a lot of people by surprise back in early March and April. Um, and I assume that that Fujitsu's no different, right? All of a sudden you have, uh, a pandemic on your hands and you've got to move nimbly and quickly. So just talk about that, if you would, that, that quick transformation that you had to make and in terms of responding to the >>Absolutely. So with AWS connect, we were able to automate and simplify the complex contact center flows that we had previously, a product of this is it's ability to now make ad hoc changes in seconds while avoiding multiple vendors to actually get those implemented. One example of this is that for you to help one of our customers move from 4,500 QS to less than 400 by actually doing call tagging attributes, instead of just creating independent flows for each one of those countries. And this mainly because of the needs from the operation to be able to quickly create reports based on countries and languages. Yeah. >>And I know you were involved or, and, and, and I might still be, I'm not sure a beta testing, uh, with some of the new, um, AWS connect features that were announced recently, you know, here at, uh, during re-invent what, what is, um, what's got you going there, you know, what, what, uh, what's caught your attention and what are you excited about seeing I go into practice on a, on a wider basis? >>Well, John, I would to say that introduction of ado list tasks has greatly helped us improve our agent productivity. We were able to see improvements of around 30% and we expect refine our customer experience even further by adding additional AWS integrations. >>Now, you mentioned, mentioned further, there's always a next step, right? Isn't there Alex. I mean, there's always, it's as good as you are now. You can't afford to sit still. I mean, that's the competitive nature of your landscape. So where do you see yourself in, in terms of rollouts in the future, or if there's an area that you think this is the next, uh, challenge for us, uh, in the, in the short term, what would that be? >>Well, that AC very good question for you to provide, uh, contact center services to around 300 diverse customers with agents speaking dozens of different languages. And we are continually looking to improve those services and experience for our customers, as well as our employees. We believe that if our employees are happy and safe and they have the tools that they need to do their work, that would result in an M in a much more improved, uh, service to our customers as such, uh, for you to source invest money, invest in heavily in the of transformation. Some of those elements would include a location agnostic delivery. This would actually allow us to create virtual teams with so employees working from Fujitsu offices while some will continue working from home. This approach will offer, uh, significantly and greater flexibility for our employees, as well as an improved efficiency of our services. >>Uh, the ability to introduce self service and automation by introducing, uh, virtual assistants, uh, robotics, uh, voice recognition, speech to text conversion, sentiment analysis. It will help us reduce the time it takes for agents or staff in repetitive tasks, allowing them to focus on the more important, uh, improvement, adding value to our customers. Being able to add, uh, tasks such as technology upgrades, uh, knowledge and data management, uh, that analytics business recommendations from our customers. This would then, uh, tied into what we're doing with improved planning, uh, as situation changes. And definitely COVID has been one example of that. Uh, Fujitsu needs to respond rapidly to ensure that we continue to provide support to all of our customers, uh, wrote a planning system, provides insights recommendations to help us deal with those changes as well as offering a level of flexibility for employees to align with their personal needs. And, uh, finally, and tying this up with those innovations that we're looking into, uh, being able to take those into employee engagement. We're introducing a proof of concept with gamification on some of our contact center, uh, desks to provide employees with a rewarding environment that offers an increase, uh, find while also doing the work reinforcing behaviors and enhancing customer satisfaction while there's certainly, um, a new >>Order, a new world, right? In, in terms of how we have to operate in a business environment. And I think you hit a key word there it's flexibility, right? Ultimately giving your employees the flexibility to still do their jobs in a very productive environment and a safe environment is critical. And it seems like Fujitsu is committed to doing that. So congratulations on that and thank you for the time today. We really appreciate it. >>Thank you very much, Sean. And thank you for the opportunity.

(techno music) >> From Orlando, Florida, it's The Cube. Covering SAP Sapphire Now 2018. Brought to you by NetApp. >> Welcome to The Cube. I'm Lisa Martin with Keith Townsend and we are at SAP Sapphire Now 2018 in Orlando. This is a massive event. Not only are there 20,000 people here but there's about a million engaging with SAP this week online. Amazing! We're joined by a Cube alumni. Welcome back to The Cube >> Thank you Lisa. Chris Hallenbeck. You are the SVP of Database and Data Management at SAP. >> What they tell me. (laughter) >> That's what they tell you. That's what your cards say? >> It is. >> Alright. Well, thanks for coming onto The Cube. So this event is enormous. Sixteen American football fields is this space. You really can close your rings. >> Well, and it is, is the energy is just crazy. It's actually different than other years. I don't know why but it really it is. >> You know yesterday, that's what Keith and I were saying yesterday. Bill McDermott really kicked things off with such enthusiasm and genuine energy. It was really amazing to see that. You don't see that with a lot of, see levels on day one. That energy was really palpable as was. >> Enterprise applications aren't that sexy huh? (crosstalk) >> Apparently they are. >> Well, apparently they are now. >> Who knew? >> Well, and that's the thing too. You guys wanting to be one of the top ten most valuable brands in the world. Up there with Apple, Google. And one of the cool things I saw yesterday on a bus out here was ERP that you can talk to and hear from. So taking this, what was an invisible product and making it now something that people can engage with like a digital assistant at home. Remarkable. >> Well, yeah. No. The user interface which has been a huge, huge thing. We have these massive UX labs throughout the world. We have ones in Palo Alto. We have ones throughout Germany and other locations. And we've been really looking at how people engage with the software. And it's not only through a screen although that's it and we win all these Red Dot awards, the Preeminent Design Award. We get those consistently now, many a year, for the work we're doing within UI which is fabulous work. But we're also again, a lot of people aren't in front of computers anymore. So how can I actually just speak into my phone and get all the information I need? How can I have the device speak to me? How can somebody wearing gloves on an assembly line, automatically they vibrate if they're reaching for the wrong bin and would have grabbed the wrong part which create a faulty defective product. So it's all built in, our actually shoes vibrating if something else happens. And so actually this interaction of sensors in two way, taking IOT data in, and then also feeding it back into signals but that's part of the interface of the software. It's not always sitting in a screen and if you are in front of a screen, they're actually pretty great to use. >> So speaking of these consumer technologies, we've had this expectation and these technologies have changed the expectations of what our business tech is. We expect to be able to do things such as, hey, say what's the latest score from last night's game. And now there's these intelligent streams of having conversations with computers. All that is powered by the data on the backend. SAP traditionally hadn't been. We talked about it on stage this morning. SAP hadn't been known for the type of company to sub at to the real-time data entry, real-time data analytics. >> Yeah. You're all about data management. We heard something on the stage this morning. What was it? Data management suite? (crosstalk) The mature database now. (crosstalk) What is that? What's that about? >> Well, now what we're finding, you know, HANA enabled these incredible use cases and originally we were all, we actually didn't run underneath SAP applications an entire database but really a data platform that people were doing these incredible innovations on. And then of course it really started to get swept underneath and it went under BW and then it became part of Sweden HANA and everyone just focused said, oh yeah, HANA is just gonna be like Netweaver. It's just a system that runs underneath SAP and we kept saying no, it's not, no, it's not. And it was sort of but that was its main, that was where it was mostly getting deployed. And then what you're actually seeing here at Sapphire is this massive breakout of technology in full use use cases. That people are using it outside even non-SAP customers are using it to solve their individual problems. Really going after that huge, that 80% of data which is non-SAP but the challenge there with is how do you handle that? Data is now sitting out in all these different clouds. HANA was known for orchestrating data but it was really designed to do it on premise because we knew not everyone's gonna put data into our system. We came in late, right. And yeah we're the fastest growing but data was sitting in Oracle, and the TIZA and that's coming up and going into data lakes, running on ADO and we could orchestrate and move that data into HANA or do it in place. Go to the cloud, it's totally different. Average customer and CIOs are telling you they have six to eight clouds and you're like, wait, how did you get to six to eight? And you're like, yeah, they've got data in storage just in Azure, in AWS, and in Google but they've also got in all these different cloud applications and a lot are from SAP but a lot aren't and yet and so companies are telling us we've lost the view of who our customer is. We've lost view of our business. Which is the opposite of what you would have expect from this data explosion and, you know, digital transformation which was like showed up and disappeared in like two years but so how do you handle that? If I have data. So much data sitting out there. IOT data in the edge, love file data sitting in object stores, I've got data in different applications, data still on Fram. How can I actually possibly move that? You can't. There's no way to put it all together in one cloud. Everyone says, oh, bring it to my cloud. It's not viable. >> Right. So how do I actually push compute, get the data I need, refine it in place, and orchestrate and move that together with the ultimate security in governance? Which is what our customers are wanting. They're saying, how Chris for our non-SAP data and SAP, can I move data for application integration? How do I do analytics? How can I pre-press data and load it into a data lake, into a data warehouse and then I'll come back and do some other cool stuff on it with data science? And that's all about by combining HANA and data hub together in a suite with deep integrations, technically from a data center readiness it's all as a service runs in the cloud but because we're SAP it's also on Prem enabled if you still want to run it that way. And it allows you to solve these huge data problems and we also help you. We bring SAPs intellectual property of data models to this so you can use things like Enterprise Architecture designer and say look we don't have a model of customer. I'm like, well yeah, what kind of industry are you in? Okay, I've got a high tech customer model pre-built for you so then you don't have to build that from scratch. We bring the things to you. So now you can get very, very quick value right from the implementation within weeks. >> And that speed is obviously essential. >> Well, how does it. (crosstalk) >> HANA's a terror, which it's known for. >> But you're right, sorry Keith, you're right that in the consumer world because we have access to everything everywhere from so many devices, we as business people expect the same thing. >> Yeah. And so that speed is critical. You talk about, you know, multiple clouds, data in so many different sources. It's not valuable unless you can actually harness it and extract insights that may only be viable for a quarter or something like that. >> But nobody even knows where the data is and so you look at like we're about to, we were talking about HANA. I just came back and we're coming out a little bit later the year with HANA data hub 2.3 which is part of HANA data management suite and that actually has a whole metadata repository. So someone who knows what they're doing goes in and maps out where all this data is located and actually they don't have to do it all themselves, it's got heuristic-al and semantic search to automatically map and categorize data. I can then map that back to like my definition of customer or supplier and other things. Now everyone doing all the analytics and doing exactly what you're talking about Keith where can I just say into my phone, hey, someone in board meeting goes hey what were our results within two peak last year over this year and show and break that down by city and have it just pop up. Just like you say to somebody, hey high school football game, didn't those two play together? Anyone can do that on a mobile device but we don't know the data in our own company. How do you do that? And then let HANA data management suite will automatically know where the data is, orchestrate, go get it, pull it together, and deliver that back to a mobile device that you might have spoken into. >> Do you have a favorite customer that articulates just what you said? >> I do. I just actually walked out of a session. It was just and it sounds a little boring but it's incredible what people are doing. So I just walked out of a thing with the Swiss Federal Railways. Sounds boring but you know where. I live in Europe and everything is by rail, right? And so they're doing about 60 percent of the rail traffic there is passengers, 1.25 million passengers a day plus the balance of 40 percent of the trains are freight. They're having a huge problem because you use huge, it's all electrical and they're trying and so when you get up and it's growing rapidly. So they're, and they do their own power with power plants and when they go up with power plants, when they go over peak they have to spot by at just massive times a premium on that data on that. And we're actually doing this a lot of place out of rail but they also use electricity on heaters and other stuff in the cold winters and air conditioners. They're now streaming information off the trains, off of the points all the way along the signals and from all the power plants. They know peak usage. It automatically detects when they're going to go over and rather than going into the plants, it actually cuts the heaters off for a second here or there. There's heaters in all the switching equipment. They know how long they can do it. HANA managed this, this is automatically so it's IOT in but it's automatically making automated business decisions, shutting down systems programmatically, intelligently actually using machine learning and keeping it. So now what they do, so now they don't need to go out to the spot market in buy energy anymore. It has cut their electrical usage by a third. >> How much money have they saved? >> No, what's a third is how much money they've saved. The electricity is still high but they're not buying that really, really >> The premium. expensive premium and so you're streaming data, it's all over, it's all happening in real time, and it's automatically kicking out business processes without human intervention. And then it's a platform for them where they're adding all this new capability to save in other ways and so it's just, you know, simple but clean really good use. Good for the planet. It's great for the customers. And now they have, and by the way, when you hit those peaks, that's when they short-out systems and that's when trains stall out. So actually you're getting better servicing of the trains. So, yeah, it's good storage. >> So edge core cloud, great breakdown of kind of the use case. The data is being collected at the edge. Data may not even be collected in a SAP system? (crosstalk) We're doing great! >> It's reality. >> It is reality and one of the things that I think architecturally that enterprises have a hard time wrapping their head around, HANA in-memory database defeats latency when you're inside the database, when you're inside of the data center, however you were thinking about HANA data management. How does the in-memory database impact and data management impact data retrieved from the edge? Help explain the importance of metadata and willing down that data so that we can get it back to the cloud and process their important data. >> Keith, it's a great question. Sometimes, HANA is not, you know. Although we like to go it's a hammer and we think everything's a nail but sometimes you don't which is why we have data hub. And it has unique capabilities for doing something called data pipelines and movement. So we can actually do all the data transformation movement calling tensor flow in flight. We do this as the data is in movement so we're actually doing all of that processing as it's moving through. If you need extra horsepower and want to combine different data types and there's certain capabilities pipeline engines don't solve well. HANA is a service which HANA is now completely cloud native. They can actually bring up HANA in a few seconds. It will take the data flow in, compute it, it's not being used as database, it's a compute layer out at the edge, the data flows out to move on to the next step usually via a data pipeline from data hub and that service gets shut off. So you just pay from small compute when you need to bring out the big guns and then it moves on. And maybe that data never comes back into a HANA system, maybe it does, but you're using the technological underpinnings of in-memory computing in this way as just literally a flow through compute engine. >> And I think that's the disconnect a lot of organizations have because you associate s4 bases, BW, all these applications on top of the database. They don't think of HANA as something that you can spin up, spin down. >> But that's brand-new and that is what we just announced and went live last week. So HANA was, there's traditional on-prem system, bare-metal, it run virtualized but I mean talking about big arm running HANA systems. Now to actually have it, so HANA as a service came up. We rewrote the entire thing to make it completely cloud native and orchestrated. It's all containerized in elastic. It runs, it came up last week running an AWS and available also in GCP. Our target is a little bit later this year. I always have to use a safe harbor language. It'll be coming up on, it'll be coming up in Azure and after all the rest of SAPs data centers and then also coming out and in Asia through Huawei and coming up in those data centers as well as some others we have planned. And that's where you actually get this fully elastic HANA that's able to come up and come down automatically. >> So this massive transformation that you guys have achieved in 46 years, say 46 years young, 390,000 customers. >> Yeah. SAP didn't get to where it is without having a really robust symbiotic partner relationship ecosystem. We're here in the NetApp booth. There's a 150 partner sessions alone at Sapphire this week. Talk to us a little bit about how the partner ecosystem is helping you guys give customers the flexibility and the choice that they need. >> Yeah, no, and it is. SAP can't do everything. And so a lot of the aspects are that we look at in very different ways. Of course, some companies and the big corporations we deal with need strategic SIs, these strategic integrators to do consulting and other pieces and we work really closely with them on and they have specialized practices and other things on both HANA. They're extending out into the HANA data management suite. We do the same thing since we realize you need boutiques. We're the fastest geospatial engine in the world but that's a very niche piece although geospatials may be the hottest data type out there happening right now. Those are very specialized boutique firms. So we work with all of those and to help our customers when they need that. So we work with a lot of specialists. We work boutiques but we couldn't do this without hardware partners, with storages which is why we allow. There's still a lot of folks running on Prem. So we still have to have all these things so we have HANA tailor data center integration so you can certify your systems like NetApp. You can certify everything else on prem so you don't have to rebuy new hardware. Use what you have. I'm not trying to get you to buy a bunch of new appliances. And then the other one is a lot of is via and OEMs have started building out on HANA but now what they really want to do is go directly on HDMS as the cloud offering because it runs both in any cloud, which is a very unique differentiator that we run in every major cloud out there, as well as coming back and running on-premise. They can play their applications very risk-free with the extreme security and governance we're providing within that stack to build applications that they want to sell and use for enterprises. >> So you've been with SAP about six years you said and even Bill McDermott said in his keynote on day one, biggest Sapphire ever. You've seen a tremendous amount of growth. The momentum here is so palpable. The types of validation that SAP is getting through the voice of the customer, through partners like Netta, the different partner ecosystem. That validation is electric. >> Yeah. >> What excites you about everything that was just announced in the last couple of days about the rest of 2018? Where do you go from here? >> Oh my god! Okay, it's like asking me to pick my favorite child. (crosstalk) But, you know, honestly I get to. You get to see the innovations that I still enjoy. I love the full use use cases because I'm like a compute guy at heart but I see all the applications that we've done in these demonstrations. The fact that people have applications that are giving all of the analytics in line with the transactions on these gorgeous UIs. I mean you run these things on a mobile device that means the data layer has 20 milliseconds to actually not only grab the data but to do all the predictive analytics and everything you see to give you that nice two second screen to screen time on your mobile device and that's what we've worked for six years to enable. And now we're seeing that potential coming out at places like Swiss Rail. Just was talking with Gustav Rossi through the biggest cancer research labs and hospitals throughout all of Europe. They're doing all this genomic research, personalized medicine for cancer patients throughout Europe using HANA. I didn't even know about it, you know, or other ones we talked about beef farmers. Talking about smart farming throughout all the Netherlands. Reducing pesticide use, water usage dramatically down, and they increased yields by 10 percent. I mean and they're doing this on native HANA. So this area for me, the excitement of people and busting out of the SAP core traditional CIO market and moving into this 80% of data is to me exciting that people are seeing that HANA is not just an SAP appliance but it's really a general-purpose data platform for these innovation use cases. >> Helping customers change their business, change industries, save lives, pretty cool stuff. >> Yeah, I think so. >> Chris, thank you so much for stopping by The Cube and sharing with us your enthusiasm and your excitement for what you're doing at SAP. We appreciate it. >> Well, thank you very much. This was awesome. Thank you guys. >> We want to thank you for watching The Cube. Lisa Martin with Keith Townsend at SAP Sapphire 2018. Thanks for watching! (techno music)

>> Voiceover: Live from Las Vegas, it's theCUBE. Covering Informatica World 2018, brought to you by Informatica. >> Hey, welcome back everyone, we're here live at the Venetian, we're at the Sands Convention Center, Venetian, the Palazzo, for Informatica World 2018. I'm John Furrier, with Peter Burris, my co-host with you. Our next guest, Arun Varadarajan, who's the VP of AI and Analytics at Cognizant. Great to see you. It's been awhile. Thanks for coming on. >> Thank you. Thank you John, it's wonderful meeting you again. >> So, last time you were on was 2015 in the queue. We were at the San Francisco, where the event was. You kind of nailed the real time piece; also, the disruption of data. Look ing forward, right now, we're kind of right at the spot you were talking about there. What's different? What's new for you? ASI data's at the center of the value preposition. >> Arun: Yep. People are now realizing, I need to have strategic data plan, not just store it, and go do analytics on it. GDPR is a signal; obviously we're seeing that. What's new? >> So, I think a couple of things, John. One is, I think the customers have realized that there is a need to have a very deliberate approach. Last time, when we spoke, we spoke about digital transformation; it was a cool thing. It had this nice feel to it. But I think what has happened in the last couple of years is that we've been able to help our clients understand what exactly is digital transformation, apart from it being a very simple comparative tactic to deal with the fact that digital natives are, you know, barking down your path. It also is an opportunity for you to really reimagine your business architecture. So, what we're telling our clients is that when you're thinking about digital transformation, think of it from a 3-layer standpoint, the first layer being your business model itself, right? Because, if you're a traditional taxi service, and you're dealing with the Uber war, you better reimagine your business model. It starts there. And then, if your business model has to change to compete in the digital world, your operating model has to be extremely aligned to that new business model paradigm that you've defined. And, to that, if you don't have a technology model that is adapting to that change, none of this is going to happen. So, we're telling our clients, when you think about digital transformation, think of it from these three dimensions. >> It's interesting, because back in the old days, your technology model dictated what you could do. It's almost flipped around, where the business model is dictating the direction. So, business model, operating model, technology model. Is that because technology is more versatile? Or, as Peter says, processes are known, and you can manage it? It used to be, hey, let's pick a technology decision. Which database, and we're off to the races. Now it seems to be flipped around. >> There are two reasons for that. One is, I think, technology itself has proliferated so much that there are so many choices to be made. And if you start looking at technology first, you get kind of burdened by the choices you need to make. Because, at the end of the day, the choice you make on technology has to have a very strong alignment and impact to business. So, what we're telling our clients is, choices are there; there are plenty of choices. There are compute strategies available that are out there. There's new analytical capabilities. There's a whole lot of that. But if you do not purpose and engineer your technology model to a specific business objective, it's lost. So, when we think about business architecture, and really competing in the digital space, it's really about you saying, how do I make sure that my business model is such that I can thwart the competition that is likely to come from digital natives? You saw Amazon the other day, right? They bought an insurance company. Who knows what they're going to buy next? My view is that Uber may buy one of the auto companies, and completely change the car industry. So, what does Ford do? What does General Motors do? And, if they're going to go about this in a very incremental fashion, my view is that they may not exist. >> So, we have been in our research arguing that digital transformation does mean something. We think that it's the difference between a business and a digital business is the role that data plays in a digital 6business, and whether or not a business treats data as an asset. Now, in every business, in every business strategy, the most simple, straightforward, bottom-line thing you can acknowledge is that businesses organize work around assets. >> John: Yep. >> So, does it comport with your observation that, to many respects, what we're talking about here is, how are we reinstitutionalizing work around data, and what impact does that have on our business model, our operating model, and our technology selection? Does that line up for you? >> Totally, totally. So, if you think about business model change, to me, it starts by re-imagining your engagement process with your customers. Re-imagining customer experience. Now, how are you going to be able to re-imagine customer experience and customer engagement if you don't know your customer? Right? So, the first building block in my mind is, do you have customer intelligence? So, when you're talking about data as an asset, to me, the asset is intelligence, right? So, customer intelligence, to me, is the first analytical building block for you to start re-imagining your business model. The second block, very clearly, is fantastic. I've re-imagined customer experience. I've re-imagined how I am going to engage with my customer. Is your product, and service, intelligent enough to develop that experience? Because, experience has to change with customers wanting new things. You know, today I was okay with buying that item online, and getting the shipment done to me in 4 days. But, that may change; I may need overnight shipping. How do you know that, right? Are you really aware of my preferences, and how quickly is your product and service aligning to that change? And, to your point, if I have customer intelligence, and product intelligence sorted out, I better make sure that my business processes are equally capable of institutionalizing intelligence. Right? So, my process orchestration, whether it's my supply chain, whether it's my auto management, whether it's my, you know, let's say fulfillment process; all of these must be equally intelligent. So, in my mind, these are three intelligent blocks: there's customer intelligence, product intelligence, and operations intelligence. If you have these three building blocks in place, then I think you can start thinking about what should your new data foundation look like. >> I want to take that and overlay kind of like, what's going on in the landscape of the industry. You have infrastructure world, which you buy some rack and stack the servers; clouds now on the scene, so there's overlapping there. We used to have a big data category. You know, ADO; but, that's now AI and machine learning, and data ware. It's kind of its own category, call it AI. And then, you have kind of emerging tech, whether you call, block chain, these kind of... confluence of all these things. But there's a data component that sits in the center of all these things. Security, data, IOT, traverse infrastructure, cloud, the classic data industry, analytics, AI, and emerging. You need data that traverses all these new environments. How does someone set up their architecture so that, because now I say, okay, I got a dat big data analytics package over here. I'm doing some analytics, next gen analytics. But, now I got to move data around for its cloud services, or for an application. So, you're seeing data as to being architected to be addressable across multiple industries. >> Great point John. In fact, that leads logically to the next thing that me and my team are working on. So we are calling it the Adaptive Data Foundation. Right? The reason why we chose the word adaptive is because in my mind it's all about adapting to change. I think Chal Salvan, or somebody said that the survival of the fittest is not, the survival is not of the survival of the fittest or the survival of the species that is intelligent, but it's the survival of those who can adapt to change, right? To me, your data foundation has to be super adaptive. So what we've done is, in fact, my notion, and I keep throwing this at you every time I meet you, in my opinion, big data is legacy. >> John: Yeah, I would agree with that. >> And its coming.. >> John: The debate. >> It's pretty much legacy in my mind. Today it's all about scale-out, responsive, compute. The data world. Now, if you looked at most of the architectures of the past of the data world, it was all about store and forward. Right? I would, it's a left to right architecture. To me it's become a multi-directional architecture. Therefore what we have done is, and this is where I think the industry is still struggling, and so are our customers. I understand I need to have a new modern data foundation, but what does that look like? What does it feel like? So with the Adaptive Data Foundation... >> They've never seen it before by the way. >> They have not seen it. >> This is new. >> They are not able to envision it. >> It is net new. >> Exactly. They're not able to envision it. So what I tell my clients is, if you really want to reimagine, just as you're reimagining your business model, your operating model, you better reimagine your data model. Is your data model capable of high velocity resolutions? Whether it's identity resolution of a client who's calling in. Whether it's the resolution of the right product and service to deliver to the client. Whether it's your process orchestration, they're able to quickly resolve that this data, this distribution center is better capable of servicing their customer need. You better have that kind of environment, right? So, somebody told me the other day that Amazon can identify an analytical opportunity and deliver a new experience and productionize it in 11.56 seconds. Today my customers, on average, the enterprise customers, barely get to have a reasonable release on a monthly basis. Forget about 11.56 seconds. So if they have to move at that kind of velocity, and that kind of responsiveness, they need to reimagine their data foundation. What we have done is, we have tried to break it down into three broad components. The first component that they're saying is that you need a highly responsive architecture. The question that you asked. And a highly responsive architecture, we've defined, we've got about seven to eight attributes that defines what a responsive architecture is. And in my mind, you'll hear a lot of, I've been hearing a lot of this that a friend, even in today's conference, people are saying, 'Oh, its going to be a hybrid world. There's going to be Onprim, there's going to be cloud, there's going to be multicloud. My view is, if you're going to have all of that mess, you're going to die, right? So I know I'm being a little harsh on this subject, but my view is you got to move to a very simplified responsive architecture right up front. >> Well you'd be prepared for any architecture. >> I've always said, we've debated this many times, I think it's a cloud world, public cloud, everything. Where the data center on premise is a huge edge. Right, so? If you think of the data center as an edge, you can say okay, it's a large edge. It's a big fat edge. >> Our fundamentalists, I don't think it exists. Our fundamental position is data increasingly, the physical realities of data, the legal realities of data, the intellectual property control realities of data, the cost realities of data are going to dictate where the processing actually takes place. There's going to be a tendency to try to move the activity as close to the data as possible so you don't have to move the data. It's not in opposition, but we think increasingly people are going to not move the data to the cloud, but move the cloud to the data. That's how we think. >> That's an interesting notion. My view is that the data has to be really close to the source of position and execution, right? >> Peter: Yeah. Data has got to be close to the activity. >> It has to be very close to the activity. >> The locality matters. >> Exactly, exactly, and my view is, if you can, I know it's tough, but a lot of our clients are struggling with that, I'm pushing them to move their data to the cloud, only for one purpose. It gives them that accessibility to a wide ranging of computer and analytical options. >> And also microservices. >> Oh yeah. >> We had a customer on earlier who's moved to the cloud. This is what we're saying about the edge being data centered. Hybrid cloud just means you're running cloud operations. Which just means you got to have a data architecture that supports cloud operations. Which means orchestration, not having siloed systems, but essentially having these kind of, data traversal, but workload management, and I think that seems to be the consistency there. This plays right into what you're saying. That adaptive platform has to enable that. >> Exactly. >> If it forecloses it, then you're missing an opportunity. I guess, how do you... Okay tell me about a customer where you had the opportunity to do the adaptive platform, and they say no, I want a silo inside my network. I got the cloud for that. I got the proprietary system here. Which is eventually foreclosing their future revenue. How do you handle that scenario? >> So the way we handle that scenario, is again, focusing on what the end objective, that the client has, from an analytical opportunity, respectfully. What I mean by that is that semi-customer says I need to be significantly more responsive in my service management, right? So if he says I want to get that achieved, then what we start thinking about is, what is that responsive data architecture that can tell us a better outcome because like you said, and you said, there's stuff on the data center, there's stuff all over the place, it's going to be difficult to take that all away. But can I create a purpose for change? Many times you need a purpose for change. So the purpose being if I can get to a much more intelligent service management framework, I will be able to either take cost out or I can increase my revenue through services. It has to be tied to an outcome. So then the conversation becomes very easy because you're building a business case for investing in change, resulting in a measurable, business outcome. So that engineer to purpose is the way I'm finding it easier to have that conversation. And I'm telling the plan, keep what you have so you've got all the speckety messes somebody said, right? You've got all of the speckety mess out there. Let us focus on, if there are 15 data sets, that we think are relevant for us to deliver service management intelligence, let's focus on those 15 data sets. Let's get that into a new scalable, hyper responsive modern architecture. Then it becomes easier. Then I can tell the customer, now we have created an equal system where we can truly get to the 11.56 seconds analytical opportunity getting productionized. Move to an experiment as a service. That's another concept. So all of that, in my opinion John, is if he can put a purpose around it, as opposed to saying let's rip and replay, let's do this large scale transformation program, those things cost a lot of money. >> Well the good news is containers and Cubernetties is stowing away to get those projects moving cloud natives as fast as possible. Love the architecture vision. Love to fault with you on that. Great conversation. I think that's a path, in my opinion. Now short-term, the house in on fire in many areas. I want to get your thoughts on this final question. GDPR, the house is on fire, it's kind of critical, it's kind of tactical. People don't like freaking out. Saying okay, saying what does this mean? Okay, it's a signal, it is important. I think it's a technical mess. I mean where's the data? What schema? John Furrier, am I J Furrier, or Furrier, John? There's data on me everywhere inside the company. It's hard. >> Arun: It is. >> So, how are you guys helping customers and navigate the landscape of GDPR? >> GDPR is a whole, it's actually a much bigger problem than we all thought it was. It is securing things at the source system because there's volatibilities of source system. Forget about it entering into any sort of mastering or data barrels. They're securing its source, that is so critical. Then, as you said, the same John Furrier, who was probably exposed to GDPR is defined in ten different ways. How do I make sure that those ten definitions are managed? >> Tells you, you need an adaptive data platform to understands. >> So right now most of our work, is just doing that impactive analysis, right? Whether it's at a source system level, it has data coverance issues, it has data security issues, it has mastering issues. So it's a fairly complex problem. I think customers are still grappling with it. They're barely, in my opinion, getting to the point of having that plan because May 18, 2018 May, was supposed to, for you to show evidence of a plan. So I think there... >> The plan is we have no plan. >> Right, the plan of the plan, I guess is what they're going to show. It may, as opposed to the plan. >> Well I'm sure it's keeping you guys super busy. I know it's on everyone's mind. We've been talking a lot about it. Great to have you on again. Great to see you. Live here at Informatica World. Day one of two days of coverage at theCUBE here. In Las Vegas, I'm John here with Peter Burris with more coverage after this short break. (techno music)

>> Announcer: Live from Boston Massachusetts this is theCube. Covering Spark Summit East 2017, brought to you by Databricks. Now here are your hosts Dave Vellante and George Gilbert. >> Welcome back to theCube, everybody, we're here in Boston. The Cube is the worldwide leader in live tech coverage. This is Spark Summit, hashtag #SparkSummit. And Robbie Strickland is here. He's the Vice President of Engines & Pipelines, I love that title, for the Watson Data Platform at IBM Analytics, formerly with The Weather Company that was acquired by IBM. Welcome to you theCube, good to see you. >> Thank you, good to be here. >> So, it's my standing tongue-in-cheek line is the industry's changing, Dell buys EMC, IBM buys The Weather Company. [Robbie] That's right. >> Wow! That sort of says it all, right? But it was kind of a really interesting blockbuster acquisition. Great for the folks at The Weather Company, great for IBM, so give us the update. Where are we at today? >> So, it's been an interesting first year. Actually, we just hit our first anniversary of the acquisition and a lot has changed. Part of my role, new role at IBM, having come from The Weather Company, is a byproduct of the two companies bringing our best analytics work and kind of pulling those together. I don't know if we have some water but that would be great. So, (coughs) excuse me. >> Dave: So, let me chat for a bit. >> Thanks. >> Feel free to clear your throat. So, you were at IBM, the conference at the time was called IBM Insight. It was the day before the acquisition was announced and we had David Kenny on. David Kenny was the CEO of The Weather Company. And I remember we were talking, and I was like, wow, you have such an interesting business model. Off camera, I was like, what do you want to do with this company, you guys are like prime. Are you going public, you going to sell this thing, I know you have an MBA background. And he goes, "Oh, yeah, we're having fun." Next day was the announcement that IBM bought The Weather Company. I saw him later and I was like, "Aha!" >> And now he's the leader of the Watson Group. >> That's right. >> Which is part of our, The Weather Company joined The Watson Group. >> And The Cloud and analytics groups have come together in recognition that analytics and The Cloud are peanut butter and jelly. >> Robbie: That's absolutely right. >> And David's running that organization, right? >> That is absolutely right. So, it's been an exciting year, it's been an interesting year, a lot of challenges. But I think where we are now with the Watson Data Platform is a real recognition that the use dase where we want to try to make data and analytics and machine learning and operationalizing all of those, that that's not easy for people. And we need to make that easy. And our experience doing that at The Weather Company and all the challenges we ran into have informed the organization, have informed the road map and the technologies that we're using to kind of move forward on that path. >> And The Watson Data Platform was announced in, I believe, October. >> Robbie: That's right. >> You guys had a big announcement in New York City. And you took many sort of components that were viewed as individual discreet functions-- >> Robbie: That's right. >> And brought them together in a single data pipeline. Is that right? >> Robbie: That's right. >> So, maybe describe that a little bit for our audience. >> So, the vision is, you know, one of the things that's missing in the market today is the ability to easily grab data from some source, whether it's a database or a Kafka stream, or some sort of streaming data feed, which is actually something that's often overlooked. Usually you have platforms that are oriented around streaming data, data feeds, or oriented around data at rest, batch data. One of the things that we really wanted to do was sort of combine those two together because we think that's really important. So, to be able to easily acquire data at scale, bring it into a platform, orchestrate complex workflows around that, with the objective, of course, of data enrichment. Ultimately, what you want to be able to do is take those raw signals, whatever they are, and turn that into some sort of enriched data for your organization. And so, for example, we may take signals in from a mobile app, things like beacons, usage beacons on a mobile app, and turn that into a recommendation engine so we can feed real time content decisions back into a mobile platform. Well, that's really hard right now. It requires lots of custom development. It requires you to essentially stitch together your pipeline end to end. It might involve a machine learning pipeline that runs a training pipeline. It might involve, it's all batch oriented, so you land your data somewhere, you run this machine learning pipeline maybe in Spark or ADO or whatever you've got. And then the results of that get fed back into some data store that gets merged with your online application. And then you need to have a restful API or something for your application to consume that and make decisions. So, our objective was to take all of the manual work of standing up those individual pieces and build a platform where that is just, that's what it's designed to do. It's designed to orchestrate those multiple combinations of real time and batch flows. And then with a click of a button and a few configuration options, stand up a restful service on top of whatever the results are. You know, either at an interim stage or at the end of the line. >> And you guys gave an example. You actually showed a demo at the announcement. And I think it was a retail example, and you showed a lot of what would traditionally be batch processes, and then real time, a recommendation came up and completed the purchase. The inference was this is an out of the box software solution. >> Robbie: That's right. >> And that's really what you're saying you've developed. A lot of people would say, oh, it's IBM, they've cobbled together a bunch of their old products, stuck them together, put an abstraction layer on, and wrapped a bunch of services around it. I'm hearing from you-- >> That's exactly, that's just WebSphere. It's WebSphere repackaged. >> (laughing) Yeah, yeah, yeah. >> No, it's not that. So, one of the things that we're trying to do is, if you look at our cloud strategy, I mean, this is really part and parcel, I mean, the nexus of the cloud strategy is the Watson Data Platform. What we could have done is we could have said let's build a fantastic cloud and compete with Amazon or Google or Microsoft. But what we realized is that there is a certain niche there of people who want to take individual services and compose them together and build an application. Mostly on top of just raw VMs with some additional, you know, let's stitch together something with Lambda or stitch together something with SQS, or whatever it may be. Our objective was to sort of elevate that a bit, not try to compete on that level. And say, how do we bring Enterprise grade capabilities to that space. Enterprise grade data management capabilities end-to-end application development, machine learning as a first class citizen, in a cohesive experience. So that, you know, the collaboration is key. We want to be able to collaborate with business users, data scientists, data engineers, developers, API developers, the consumers of the end results of that, whether they be mobile developers or whatever. One of the things that is sort of key, I think, to the vision is that these roles that we've traditionally looked at. If you look at the way that tool sets are built, they're very targeted to specific roles. The data engineer has a tool, the data scientist has a tool. And what's been the difficult part is the boundaries between those have been very firm and the collaboration has been difficult. And so, we draw the personas as a Venn diagram. Because it's very difficult, especially if you look at a smaller company, and even sometimes larger companies, the data engineer is the data scientist. The developer who builds the mobile application is the data scientist. And then in some larger organizations, you have very large teams of data scientists that have these artificial barriers between the data scientist and the data engineer. So, how do we solve both cases? And I think the answer was for us a platform that allows for seamless collaboration where there is not these clean lines between the personas, that the tool sets easily move from one to the other. And if you're one of those hybrid people that works across lines, that the tool feels like it's one tool for you. But if you're two different teams working together, that you can easily hand off. So, that was one of the key objectives we're trying to answer. >> Definitely an innovative component of the announcement, for sure. Go ahead, George. >> So, help us sort of bracket how mature this end-to-end tool suite is in terms of how much of the pipeline it addresses. You know, from the data origin all the way to a trained model and deploying that model. Sort of what's there now, what's left to do. >> So, there are a few things we've brought to market. Probably the most significant is the data science experience. The data science experience is oriented around data science and has, as its sort of central interface, Jupyter Notebooks. Now, as well as, we brought in our studio, and those sorts of things. The idea there being that we'll start with the collaboration around data scientists. So, data scientists can use their language of choice, collaborate around data sets, save out the results of their work and have it consumed either publicly by some other group of data scientists. But the collaboration among data scientists, that was sort of step one. There's a lot of work going on that's sort of ongoing, not ready to bring to market, around how do we simplify machine learning pipelines specifically, how do we bring governance and lineage, and catalog services and those sorts of things. And then the ingest, one of the things we're working on that we have brought to market is our product called Lift which connects, as well. And that's bringing large amounts of data easily into the platform. There are a few components that have sort of been brought to market. dashDB, of course, is a key source of data clouded. So, one of the things that we're working on is some of these existing technologies that actually really play well into the eco system, trying to tie them well together. And then add the additional glue pieces. >> And some of your information management and governance components, as well. Now, maybe that is a little bit more legacy but they're proven. And I don't know if the exits and entries into those systems are as open, I don't know, but there's some capabilities there. >> Speaking of openness, that's actually a great point. If you look at the IIG suite, it's a great On-Premise suite. And one of the challenges that we've had in sort of past IBM cloud offerings is a lot of what has been the M.O. in the past is take a great On-Prem solution and just try to stand it up as a service in the cloud. Which in some cases has been successful, in other cases, less so. One of the things we're trying to look at with this platform is how do we leverage (a) open source. So that whatever you may already be running open source on, Prem or in some other provider, that it's very easy to move your workloads. So, we want to be able to say if you've got 10,000 lines of fraud detection code to map produce. You don't need to rewrite that in anything. You can just move it. And the other thing is where our existing legacy tech doesn't necessarily translate well to the cloud, our first strategy is see if there's any traction around an existing open source project that satisfies that need, and try to see if we can build on that. Where there's not, we go cloud first and we build something that's tailor made to come out. >> So, who's the first one or two customers for this platform? Is it like IBM Global Business Services where they're building the semi-custom industry apps? Or is it the very, very big and sophisticated, like banks and Telcos who are doing the same? Or have you gotten to the point where you can push it out to a much wider audience? >> That's a great question, and it's actually one that is a source of lots of conversation internally for us. If you look at where the data science experience is right now, it's a lot of individual data scientists, you know, small companies, those sorts of things coming together. And a lot of that is because some of the sophistication that we expect for Enterprise customers is not quite there yet. So, we wouldn't expect Enterprise customers to necessarily be onboarded as quickly at the moment. But if we look at sort of the, so I guess there's maybe a medium term answer and a long term answer. I think the long term answer is definitely the Enterprise customers, you know, leveraging IBM's huge entry point into all of those customers today, there's definitely a play to be made there. And one of the things that we're differentiating, we think, over an AWS or Google, is that we're trying to answer that use case in a way that they really aren't even trying to answer it right now. And so, that's one thing. The other is, you know, going beta with a launch customer that's a healthcare provider or a bank where they have all sorts of regulatory requirements, that's more complicated. And so, we are looking at, in some cases, we're looking at those banks or healthcare providers and trying to carve off a small niche use case that doesn't actually fall into the category of all those regulatory requirements. So that we can get our feet wet, get the tires kicked, those sorts of things. And in some cases we're looking for less traditional Enterprise customers to try to launch with. So, that's an active area of discussion. And one of the other key ones is The Weather Company. Trying to take The Weather Company workloads and move The Weather Company workloads. >> I want to come back to The Weather Company. When you did that deal, I was talking to one of your executives and he said, "Why do you think we did the deal?" I said, "Well, you've got 1500 data scientists, "you've got all this data, you know, it's the future." He goes, "Yeah, it's also going to be a platform "for IOT for IBM." >> Robbie: That's right. >> And I was like, "Hmmm." I get the IOT piece, how does it become a platform for IBM's IOT strategy? Is that really the case? Is that transpiring and how so? >> It's interesting because that was definitely one of the key tenets behind the acquisition. And what we've been working on so hard over the last year, as I'm sure you know, sometimes boxes and arrows on an architecture diagram and reality are more challenging. >> Dave: (laughing) Don't do that. >> And so, what we've had to do is reconcile a lot of what we built at The Weather Company, existing IBM tech, and the new things that were in flight, and try to figure out how can we fit all those pieces together. And so, it's been complicated but also good. In some cases, it's just people and expertise. And bringing those people and expertise and leaving some of the software behind. And other cases, it's actually bringing software. So, the story is, obviously, where the rubber meets the road, more complicated than what it sounds like in the press release. But the reality is we've combined those teams and they are all moving in the same direction together with various bits and pieces from the different teams. >> Okay, so, there's vision and then the road map to execute on that, and it's going to unfold over several years. >> Robbie: That's right. >> Okay, good. Stuff at the event here, I mean, what are you seeing, what's hot, what's going on with Spark? >> I think one of the interesting things with what's going on with Spark right now is a lot of the optimizations, especially things around GPUs and that. And we're pretty excited about that, being a hardware manufacturer, that's something that is interesting to us. We run our own cloud. Where some people may not be able to immediately leverage those capabilities, we're pretty excited about that. And also, we're looking at some of those, you know, taking Spark and running it on Power and those sorts of things to try to leverage the hardware improvements. So, that's one of the things we're doing. >> Alright, we have to leave it there, Robbie. Thanks very much for coming on theCube, really appreciate it. >> Thank you. >> You're welcome. Alright, keep it right there, everybody. We'll be right back with our next guest. This is theCube. We're live from Spark Summit East, hashtag #SparkSummit. Be right back. >> Narrator: Since the dawn of The Cloud, theCube.